Can the Entire Function of the Foot Be Concentrated in the Forefoot Area during the Running Stance Phase? A Finite Element Study of Different Shoe Soles.

The goal of this study was to use the finite element (FE) method to compare and study the differences between bionic shoes (BS) and normal shoes (NS) forefoot strike patterns when running. In addition, we separated the forefoot area when forefoot running as a way to create a small and independent area of instability. An adult male of Chinese descent was recruited for this investigation (age: 26 years old; body height: 185 cm; body mass: 82 kg) (forefoot strike patterns). We analyzed forefoot running under two different conditions through FE analysis, and used bone stress distribution feature classification and recognition for further analysis. The metatarsal stress values in forefoot strike patterns with BS were less than with NS. Additionally, the bone stress classification of features and the recognition accuracy rate of metatarsal (MT) 2, MT3 and MT5 were higher than other foot bones in the first 5%, 10%, 20% and 50% of nodes. BS forefoot running helped reduce the probability of occurrence of metatarsal stress fractures. In addition, the findings further revealed that BS may have important implications for the prevention of hallux valgus, which may be more effective in adolescent children. Finally, this study presents a post-processing method for FE results, which is of great significance for further understanding and exploration of FE results.

New Insights Optimize Landing Strategies to Reduce Lower Limb Injury Risk.

Single-leg landing (SL) is often associated with a high injury risk, especially anterior cruciate ligament (ACL) injuries and lateral ankle sprain. This work investigates the relationship between ankle motion patterns (ankle initial contact angle [AICA] and ankle range of motion [AROM]) and the lower limb injury risk during SL, and proposes an optimized landing strategy that can reduce the injury risk. To more realistically revert and simulate the ACL injury mechanics, we developed a knee musculoskeletal model that reverts the ACL ligament to a nonlinear short-term viscoelastic mechanical mechanism (strain rate-dependent) generated by the dense connective tissue as a function of strain. Sixty healthy male subjects were recruited to collect biomechanics data during SL. The correlation analysis was conducted to explore the relationship between AICA, AROM, and peak vertical ground reaction force (PVGRF), joint total energy dissipation (TED), peak ankle knee hip sagittal moment, peak ankle inversion angle (PAIA), and peak ACL force (PAF). AICA exhibits a negative correlation with PVGRF (r = -0.591) and PAF (r = -0.554), and a positive correlation with TED (r = 0.490) and PAIA (r = 0.502). AROM exhibits a positive correlation with TED (r = 0.687) and PAIA (r = 0.600). The results suggested that the appropriate increases in AICA (30° to 40°) and AROM (50° to 70°) may reduce the lower limb injury risk. This study has the potential to offer novel perspectives on the optimized application of landing strategies, thus giving the crucial theoretical basis for decreasing injury risk.

Structural and Organizational Strategies of Locomotor Modules during Landing in Patients with Chronic Ankle Instability.

BACKGROUND: Human locomotion involves the coordinated activation of a finite set of modules, known as muscle synergy, which represent the motor control strategy of the central nervous system. However, most prior studies have focused on isolated muscle activation, overlooking the modular organization of motor behavior. Therefore, to enhance comprehension of muscle coordination dynamics during multi-joint movements in chronic ankle instability (CAI), exploring muscle synergies during landing in CAI patients is imperative. METHODS: A total of 22 patients with unilateral CAI and 22 healthy participants were recruited for this research. We employed a recursive model for second-order differential equations to process electromyographic (EMG) data after filtering preprocessing, generating the muscle activation matrix, which was subsequently inputted into the non-negative matrix factorization model for extraction of the muscle synergy. Muscle synergies were classified utilizing the K-means clustering algorithm and Pearson correlation coefficients. Statistical parameter mapping (SPM) was employed for temporal modular parameter analyses. RESULTS: Four muscle synergies were identified in both the CAI and healthy groups. In Synergy 1, only the gluteus maximus showed significantly higher relative weight in CAI compared to healthy controls (p = 0.0035). Synergy 2 showed significantly higher relative weights for the vastus lateralis in the healthy group compared to CAI (p = 0.018), while in Synergy 4, CAI demonstrated significantly higher relative weights of the vastus lateralis compared to healthy controls (p = 0.030). Furthermore, in Synergy 2, the CAI group exhibited higher weights of the tibialis anterior compared to the healthy group (p = 0.042). CONCLUSIONS: The study suggested that patients with CAI exhibit a comparable modular organizational framework to the healthy group. Investigation of amplitude adjustments within the synergy spatial module shed light on the adaptive strategies employed by the tibialis anterior and gluteus maximus muscles to optimize control strategies during landing in patients with CAI. Variances in the muscle-specific weights of the vastus lateralis across movement modules reveal novel biomechanical adaptations in CAI, offering valuable insights for refining rehabilitation protocols.

Exploring biomechanical variations in ankle joint injuries among Latin dancers with different stance patterns: utilizing OpenSim musculoskeletal models.

Background: Dancers represent the primary demographic affected by ankle joint injuries. In certain movements, some Latin dancers prefer landing on the Forefoot (FT), while others prefer landing on the Entire foot (ET). Different stance patterns can have varying impacts on dancers' risk of ankle joint injuries. The purpose of this study is to investigate the differences in lower limb biomechanics between Forefoot (FT) dancers and Entire foot (ET) dancers. Method: A group of 21 FT dancers (mean age 23.50 (S.D. 1.12) years) was compared to a group of 21 ET dancers (mean age 23.33 (S.D. 0.94) years), performing the kicking movements of the Jive in response to the corresponding music. We import data collected from Vicon and force plates into OpenSim to establish musculoskeletal models for computing kinematics, dynamics, muscle forces, and muscle co-activation. Result: In the sagittal plane: ankle angle (0%-100%, p < 0.001), In the coronal plane: ankle angle (0%-9.83%, p = 0.001) (44.34%-79.52%, p = 0.003), (88.56%-100%, p = 0.037), ankle velocity (3.73%-11.65%, p = 0.017) (94.72-100%, p = 0.031); SPM analysis revealed that FT dancers exhibited significantly smaller muscle force than ET dancers around the ankle joint during the stance phase. Furthermore, FT dancers displayed reduced co-activation compared to ET dancers around the ankle joint during the descending phase, while demonstrating higher co-activation around the knee joint than ET dancers. Conclusion: This study biomechanically demonstrates that in various stance patterns within Latin dance, a reduction in lower limb stance area leads to weakened muscle strength and reduced co-activation around the ankle joint, and results in increased ankle inversion angles and velocities, thereby heightening the risk of ankle sprains. Nevertheless, the increased co-activation around the knee joint in FT dancers may be a compensatory response for reducing the lower limb stance area in order to maintain stability.

A new method applied for explaining the landing patterns: Interpretability analysis of machine learning.

As one of many fundamental sports techniques, the landing maneuver is also frequently used in clinical injury screening and diagnosis. However, the landing patterns are different under different constraints, which will cause great difficulties for clinical experts in clinical diagnosis. Machine learning (ML) have been very successful in solving a variety of clinical diagnosis tasks, but they all have the disadvantage of being black boxes and rarely provide and explain useful information about the reasons for making a particular decision. The current work validates the feasibility of applying an explainable ML (XML) model constructed by Layer-wise Relevance Propagation (LRP) for landing pattern recognition in clinical biomechanics. This study collected 560 groups landing data. By incorporating these landing data into the XML model as input signals, the prediction results were interpreted based on the relevance score (RS) derived from LRP. The interpretation obtained from XML was evaluated comprehensively from the statistical perspective based on Statistical Parametric Mapping (SPM) and Effect Size. The RS has excellent statistical characteristics in the interpretation of landing patterns between classes, and also conforms to the clinical characteristics of landing pattern recognition. The current work highlights the applicability of XML methods that can not only satisfy the traditional decision problem between classes, but also largely solve the lack of transparency in landing pattern recognition. We provide a feasible framework for realizing interpretability of ML decision results in landing analysis, providing a methodological reference and solid foundation for future clinical diagnosis and biomechanical analysis.

Effects of different contact angles during forefoot running on the stresses of the foot bones: a finite element simulation study.

Introduction: The purpose of this study was to compare the changes in foot at different sole-ground contact angles during forefoot running. This study tried to help forefoot runners better control and improve their technical movements by comparing different sole-ground contact angles. Methods: A male participant of Chinese ethnicity was enlisted for the present study, with a recorded age of 25 years, a height of 183 cm, and a body weight of 80 kg. This study focused on forefoot strike patterns through FE analysis. Results: It can be seen that the peak von Mises stress of M1-5 (Metatarsal) of a (Contact angle: 9.54) is greater than that of b (Contact angle: 7.58) and c (Contact angle: 5.62) in the three cases. On the contrary, the peak von Mises stress of MC (Medial Cuneiform), IC (Intermediate Cuneiform), LC (Lateral Cuneiform), C (Cuboid), N (Navicular), T (Tarsal) in three different cases is opposite, and the peak von Mises stress of c is greater than that of a and b. The peak von Mises stress of b is between a and c. Conclusion: This study found that a reduced sole-ground contact angle may reduce metatarsal stress fractures. Further, a small sole-ground contact angle may not increase ankle joint injury risk during forefoot running. Hence, given the specialized nature of the running shoes designed for forefoot runners, it is plausible that this study may offer novel insights to guide their athletic pursuits.

A new method proposed for realizing human gait pattern recognition: Inspirations for the application of sports and clinical gait analysis.

BACKGROUND: Finding the best subset of gait features among biomechanical variables is considered very important because of its ability to identify relevant sports and clinical gait pattern differences to be explored under specific study conditions. This study proposes a new method of metaheuristic optimization-based selection of optimal gait features, and then investigates how much contribution the selected gait features can achieve in gait pattern recognition. METHODS: Firstly, 800 group gait datasets performed feature extraction to initially eliminate redundant variables. Then, the metaheuristic optimization algorithm model was performed to select the optimal gait feature, and four classification algorithm models were used to recognize the selected gait feature. Meanwhile, the accuracy results were compared with two widely used feature selection methods and previous studies to verify the validity of the new method. Finally, the final selected features were used to reconstruct the data waveform to interpret the biomechanical meaning of the gait feature. RESULTS: The new method finalized 10 optimal gait features (6 ankle-related and 4-related knee features) based on the extracted 36 gait features (85 % variable explanation) by feature extraction. The accuracy in gait pattern recognition among the optimal gait features selected by the new method (99.81 % ± 0.53 %) was significantly higher than that of the feature-based sorting of effect size (94.69 % ± 2.68 %), the sequential forward selection (95.59 % ± 2.38 %), and the results of previous study. The interval between reconstructed waveform-high and reconstructed waveform-low curves based on the selected feature was larger during the whole stance phase. SIGNIFICANCE: The selected gait feature based on the proposed new method (metaheuristic optimization-based selection) has a great contribution to gait pattern recognition. Sports and clinical gait pattern recognition can benefit from population-based metaheuristic optimization techniques. The metaheuristic optimization algorithms are expected to provide a practical and elegant solution for sports and clinical biomechanical feature selection with better economy and accuracy.

Adaptive neuro-fuzzy inference system model driven by the non-negative matrix factorization-extracted muscle synergy patterns to estimate lower limb joint movements.

BACKGROUND AND OBJECTIVE: For patients with movement disorders, the main clinical focus is on exercise rehabilitation to help recover lost motor function, which is achieved by relevant assisted equipment. The basis for seamless control of the assisted equipment is to achieve accurate inference of the user's movement intentions in the human-machine interface. This study proposed a novel movement intention detection technology for estimating lower limb joint continuous kinematic variables following muscle synergy patterns, to develop applications for more efficient assisted rehabilitation training. METHODS: This study recruited 16 healthy males and 16 male patients with symptomatic patellar tendinopathy (VISA-P: 59.1 ± 8.7). The surface electromyography of 12 muscles and lower limb joint kinematic and kinetic data from healthy subjects and patients during step-off landings from 30 cm-high stair steps were collected. We subsequently solved the preprocessed data based on the established recursive model of second-order differential equation to obtain the muscle activation matrix, and then imported it into the non-negative matrix factorization model to obtain the muscle synergy matrix. Finally, the lower limb neuromuscular synergy pattern was then imported into the developed adaptive neuro-fuzzy inference system non-linear regression model to estimate the human movement intention during this movement pattern. RESULTS: Six muscle synergies were determined to construct the muscle synergy pattern driven ANFIS model. Three fuzzy rules were determined in most estimation cases. Combining the results of the four error indicators across the estimated variables indicates that the current model has excellent estimated performance in estimating lower limb joint movement. The estimation errors between the healthy (Angle: R2=0.98±0.03; Torque: R2=0.96±0.04) and patient (Angle: R2=0.98±0.02; Torque: R2=0.96±0.03) groups are consistent. CONCLUSION: The proposed model of this study can accurately and reliably estimate lower limb joint movements, and the effectiveness will also be radiated to the patient group. This revealed that our models also have certain advantages in the recognition of motor intentions in patients with relevant movement disorders. Future work from this study can be focused on sports rehabilitation in the clinical field by achieving more flexible and precise movement control of the lower limb assisted equipment to help the rehabilitation of patients.

Biomechanical Analysis of Latin Dancers' Lower Limb during Normal Walking.

Latin dance involves fundamental walking steps, integral to the dance process. While resembling daily walking, Latin dance demands higher balance levels, necessitating body adjustments by dancers. These adaptations affect dancers' gait biomechanics, prompting our study on gait differences between Latin dancers (LDs) and non-dancers (NDs). We enlisted 21 female Latin dancers and 21 subjects based on specific criteria. Participants executed walking tasks, with an independent sample t-test for 1-dimensional statistical parameter mapping (SPM 1d) analyzing stance phase variations between LDs and NDs. Notably, significant differences in ankle and hip external rotation were evident during the 16.43-29.47% (p = 0.015) and 86.35-100% (p = 0.014) stance phase. Moreover, pronounced distinctions in rectus Achilles tendon force (ATF) (12.83-13.10%, p = 0.049; 15.89-80.19%, p < 0.001) and Patellofemoral joint contact force (PTF) (15.85-18.31%, p = 0.039; 21.14-24.71%, p = 0.030) during stance were noted between LDs (Latin dancers) and NDs (Non-dancers). The study revealed dancers' enhanced balance attributed to external ankle rotation for dance stability, coupled with augmented Achilles tendon and patellofemoral joint strength from prolonged practice. Moreover, integrating suitable Latin dance into rehabilitation may benefit those with internal rotation gait issues.

Accurately and effectively predict the ACL force: Utilizing biomechanical landing pattern before and after-fatigue.

BACKGROUND AND OBJECTIVE: As a fundamental exercise technique, landing can commonly be associated with anterior cruciate ligament (ACL) injury, especially during after-fatigue single-leg landing (SL). Presently, the inability to accurately detect ACL loading makes it difficult to recognize the risk degree of ACL injury, which reduces the effectiveness of injury prevention and sports monitoring. Increased risk of ACL injury during after-fatigue SL may be related to changes in ankle motion patterns. Therefore, this study aims to develop a highly accurate and easily implemented ACL force prediction model by combining deep learning and the explored relationship between ACL force and ankle motion pattern. METHODS: First, 56 subjects' during before and after-fatigue SL data were collected to explore the relationship between the ankle initial contact angle (AIC), ankle range of motion (AROM) and peak ACL force (PAF). Then, the musculoskeletal model was developed to simulate and calculate the ACL force. Finally, the ACL force prediction model was constructed by combining the explored relationship and sparrow search algorithm (SSA) to optimize the extreme learning machine (ELM) and long short-term memory (LSTM). RESULTS: There was almost a stronger linear relationship between the PAF and AIC (R = -0.70), AROM (R2 = -0.61). By substituting AIC and AROM as independent variables in the SSA-ELM prediction model, the model shows excellent prediction performance because of very strong correlation (R2 = 0.9992,  MSE = 0.0023,  RMSE = 0.0474). Based on the equal scaling by combining results of SSA-ELM and SSA-LSTM, the prediction model achieves excellent performance in ACL force prediction of the overall waveform (R2 = 0.9947,  MSE = 0.0076,  RMSE = 0.0873). CONCLUSION: By increasing the AIC and AROM during SL, the lower limb joint energy dissipation can be increased and the PAF reduced, thus reducing the impact loads on the lower limb joints and reducing ACL injuries. The proposed ACL dynamic load force prediction model has low input variable demands (sagittal joint angles), excellent generalization capabilities and superior performance in terms of high accuracy. In the future, we plan to use it as an accurate ACL injury risk assessment tool to promote and apply it to a wider range of sports training and injury monitoring.

PCA of Running Biomechanics after 5 km between Novice and Experienced Runners.

Increased running experience appears to lower the risk of running-related injuries, but the mechanisms underlying this are unknown. Studying the biomechanics of runners with different running experiences before and after long-distance running can improve our understanding of the relationship between faulty running mechanics and injury. The purpose of the present study was to investigate if there were any differences in lower-limb biomechanics between runners after a 5 km run. Biomechanical data were collected from 15 novice and 15 experienced runners. Principal component analysis (PCA) with single-component reconstruction was used to identify variations in running biomechanics across the gait waveforms. A two-way repeated-measures ANOVA was conducted to explore the effects of runner and a 5 km run. Significant runner group differences were found for the kinematics and kinetics of lower-limb joints and ground reaction force (GRF) with respect to the magnitude across the stance phase. We found that novice runners exhibited greater changes in joint angles, joint moments, and GRFs than experienced runners regardless of the prolonged running session, and those patterns may relate to lower-limb injuries. The results of this study suggest that the PCA approach can provide unique insight into running biomechanics and injury mechanisms. The findings from the study could potentially guide training program developments and injury prevention protocols for runners with different running experiences.

Simulation of Lower Limb Muscle Activation Using Running Shoes with Different Heel-to-Toe Drops Using Opensim.

BACKGROUND: Although numerous studies have been conducted to investigate the acute effects of shoe drops on running kinematics and kinetic variables, their effects on muscle forces remain unknown. Thus, the primary aim of this study was to compare the muscle force, kinematics, and kinetic variables of habitually rearfoot runners with heel-to-toe drops of negative 8 mm shoes (minimalist shoes) and positive 9 mm shoes (normal shoes) during the running stance phase by using musculoskeletal modeling and simulation techniques. METHODS: Experimental data of lower limb kinematics, ground reaction force, and muscle activation from 16 healthy runners with rearfoot strike patterns were collected and analyzed in OpenSim. Using Matlab, the statistical parameter mapping paired t-test was used to compare the joint angle, moment, and muscle force waveform. RESULTS: The results revealed differences in the sagittal ankle and hip angles and sagittal knee moments between the different heel-to-toe drops of running shoes. Specifically, it showed that the negative 8 mm running shoes led to significantly smaller values than the positive 9 mm running shoes in terms of the angle of ankle dorsiflexion, ankle eversion, knee flexion, hip flexion, and hip internal and hip external rotation. The peak ankle dorsiflexion moment, ankle plantarflexion moment, ankle eversion moment, knee flexion moment, knee abduction moment, and knee internal rotation also decreased obviously with the minimalist running shoes, while the lateral gastrocnemius, Achilleas tendon, and extensor hallucis longus muscles were obviously greater in the minimalist shoes compared to normal shoes. The vastus medialis, vastus lateralis and extensor digitorum longus muscles force were smaller in the minimalist shoes. CONCLUSIONS: Runners may shift to a midfoot strike pattern when wearing negative running shoes. High muscle forces in the gastrocnemius lateral, Achilleas tendon, and flexor hallucis longus muscles may also indicate an increased risk of Achilleas tendonitis and ankle flexor injuries.

A new method proposed to explore the feline's paw bones of contributing most to landing pattern recognition when landed under different constraints.

Felines are generally acknowledged to have natural athletic ability, especially in jumping and landing. The adage "felines have nine lives" seems applicable when we consider its ability to land safely from heights. Traditional post-processing of finite element analysis (FEA) is usually based on stress distribution trend and maximum stress values, which is often related to the smoothness and morphological characteristics of the finite element model and cannot be used to comprehensively and deeply explore the mechanical mechanism of the bone. Machine learning methods that focus on feature pattern variable analysis have been gradually applied in the field of biomechanics. Therefore, this study investigated the cat forelimb biomechanical characteristics when landing from different heights using FEA and feature engineering techniques for post-processing of FEA. The results suggested that the stress distribution feature of the second, fourth metacarpal, the second, third proximal phalanx are the features that contribute most to landing pattern recognition when cats landed under different constraints. With increments in landing altitude, the variations in landing pattern differences may be a response of the cat's forelimb by adjusting the musculoskeletal structure to reduce the risk of injury with a more optimal landing strategy. The combination of feature engineering techniques can effectively identify the bone's features that contribute most to pattern recognition under different constraints, which is conducive to the grasp of the optimal feature that can reveal intrinsic properties in the field of biomechanics.

New Insights for the Design of Bionic Robots: Adaptive Motion Adjustment Strategies During Feline Landings.

Felines have significant advantages in terms of sports energy efficiency and flexibility compared with other animals, especially in terms of jumping and landing. The biomechanical characteristics of a feline (cat) landing from different heights can provide new insights into bionic robot design based on research results and the needs of bionic engineering. The purpose of this work was to investigate the adaptive motion adjustment strategy of the cat landing using a machine learning algorithm and finite element analysis (FEA). In a bionic robot, there are considerations in the design of the mechanical legs. (1) The coordination mechanism of each joint should be adjusted intelligently according to the force at the bottom of each mechanical leg. Specifically, with the increase in force at the bottom of the mechanical leg, the main joint bearing the impact load gradually shifts from the distal joint to the proximal joint; (2) the hardness of the materials located around the center of each joint of the bionic mechanical leg should be strengthened to increase service life; (3) the center of gravity of the robot should be lowered and the robot posture should be kept forward as far as possible to reduce machine wear and improve robot operational accuracy.

Explaining the differences of gait patterns between high and low-mileage runners with machine learning.

Running gait patterns have implications for revealing the causes of injuries between higher-mileage runners and low-mileage runners. However, there is limited research on the possible relationships between running gait patterns and weekly running mileages. In recent years, machine learning algorithms have been used for pattern recognition and classification of gait features to emphasize the uniqueness of gait patterns. However, they all have a representative problem of being a black box that often lacks the interpretability of the predicted results of the classifier. Therefore, this study was conducted using a Deep Neural Network (DNN) model and Layer-wise Relevance Propagation (LRP) technology to investigate the differences in running gait patterns between higher-mileage runners and low-mileage runners. It was found that the ankle and knee provide considerable information to recognize gait features, especially in the sagittal and transverse planes. This may be the reason why high-mileage and low-mileage runners have different injury patterns due to their different gait patterns. The early stages of stance are very important in gait pattern recognition because the pattern contains effective information related to gait. The findings of the study noted that LRP completes a feasible interpretation of the predicted results of the model, thus providing more interesting insights and more effective information for analyzing gait patterns.

Gait Characteristics and Fatigue Profiles When Standing on Surfaces with Different Hardness: Gait Analysis and Machine Learning Algorithms.

BACKGROUND: Longtime standing may cause fatigue and discomfort in the lower extremities, leading to an increased risk of falls and related musculoskeletal diseases. Therefore, preventive interventions and fatigue detection are crucial. This study aims to explore whether anti-fatigue mats can improve gait parameters following long periods of standing and try to use machine learning algorithms to identify the fatigue states of standing workers objectively. METHODS: Eighteen healthy young subjects were recruited to stand on anti-fatigue mats and hard ground to work 4 h, including 10 min rest. The portable gait analyzer collected walking speed, stride length, gait frequency, single support time/double support time, swing work, and leg fall intensity. A Paired sample t-test was used to compare the difference of gait parameters without standing intervention and standing on two different hardness planes for 4 h. An independent sample t-test was used to analyze the difference between males and females. The K-nearest neighbor (KNN) classification algorithm was performed, the subject's gait characteristics were divided into non-fatigued and fatigue groups. The gait parameters selection and the error rate of fatigue detection were analyzed. RESULTS: When gender differences were not considered, the intensity of leg falling after standing on the hard ground for 4 h was significantly lower than prior to the intervention (p < 0.05). When considering the gender, the stride length and leg falling strength of female subjects standing on the ground for 4 h were significantly lower than those before the intervention (p < 0.05), and the leg falling strength after standing on the mat for 4 h was significantly lower than that recorded before the standing intervention (p < 0.05). The leg falling strength of male subjects standing on the ground for 4 h was significantly lower than before the intervention (p < 0.05). After standing on the ground for 4 h, female subjects' walking speed and stride length were significantly lower than those of male subjects (p < 0.05). In addition, the accuracy of testing gait parameters to predict fatigue was medium (75%). After standing on the mat was divided into fatigue, the correct rate was 38.9%, and when it was divided into the non-intervention state, the correct rate was 44.4%. CONCLUSION: The results show that the discomfort and fatigue caused by standing for 4 h could lead to the gait parameters variation, especially in females. The use of anti-fatigue mats may improve the negative influence caused by standing for a long period. The results of the KNN classification algorithm showed that gait parameters could be identified after fatigue, and the use of an anti-fatigue mat could improve the negative effect of standing for a long time. The accuracy of the prediction results in this study was moderate. For future studies, researchers need to optimize the algorithm and include more factors to improve the prediction accuracy.

Effects of Fatigue Running on Joint Mechanics in Female Runners: A Prediction Study Based on a Partial Least Squares Algorithm.

Background: Joint mechanics are permanently changed using different intensities and running durations. These variations in intensity and duration also influence fatigue during prolonged running. Little is known about the potential interactions between fatigue and joint mechanics in female recreational runners. Thus, the purpose of this study was to describe and examine kinematic and joint mechanical parameters when female recreational runners are subject to fatigue as a result of running. Method: Fifty female recreational runners maintained running on a treadmill to induce fatigue conditions. Joint mechanics, sagittal joint angle, moment, and power were recorded pre- and immediately post fatigue treadmill running. Result: Moderate reductions in absolute positive ankle power, total ankle energy dissipation, dorsiflexion at initial contact, max dorsiflexion angle, and range of motion of the joint ankle were collected after fatigue following prolonged fatigue running. Knee joint mechanics, joint angle, and joint power remained unchanged after prolonged fatigue running. Nevertheless, with the decreased ankle joint work, negative knee power increased. At the hip joint, the extension angle was significantly decreased. The range motion of the hip joint, hip positive work and hip positive power were increased during the post-prolonged fatigue running. Conclusion: This study found no proximal shift in knee joint mechanics in amateur female runners following prolonged fatigue running. The joint work redistribution was associated with running fatigue changes. As for long-distance running, runners should include muscle strength training to avoid the occurrence of running-related injuries.

Competitive and Recreational Running Kinematics Examined Using Principal Components Analysis.

Kinematics data are primary biomechanical parameters. A principal component analysis (PCA) of waveforms is a statistical approach used to explore patterns of variability in biomechanical curve datasets. Differences in experienced and recreational runners' kinematic variables are still unclear. The purpose of the present study was to compare any differences in kinematics parameters for competitive runners and recreational runners using principal component analysis in the sagittal plane, frontal plane and transverse plane. Forty male runners were divided into two groups: twenty competitive runners and twenty recreational runners. A Vicon Motion System (Vicon Metrics Ltd., Oxford, UK) captured three-dimensional kinematics data during running at 3.3 m/s. The principal component analysis was used to determine the dominating variation in this model. Then, the principal component scores retained the first three principal components and were analyzed using independent t-tests. The recreational runners were found to have a smaller dorsiflexion angle, initial dorsiflexion contact angle, ankle inversion, knee adduction, range motion in the frontal knee plane and hip frontal plane. The running kinematics data were influenced by running experience. The findings from the study provide a better understanding of the kinematics variables for competitive and recreational runners. Thus, these findings might have implications for reducing running injury and improving running performance.

An Investigation of Differences in Lower Extremity Biomechanics During Single-Leg Landing From Height Using Bionic Shoes and Normal Shoes.

Bionic shoes utilizing an actual foot shape sole structure can alter lower limb's biomechanics, which may help in the development of specific training or rehabilitation programs. The purpose of this study was to investigate the biomechanical differences in the lower limb during a single-leg landing task using bionic shoes (BS) and normal shoes (NS). Fifteen healthy male subjects participated in this study, sagittal, and frontal plane data were collected during the landing phase (drop landing from 35 cm platform). Our study showed that BS depicted a significantly greater minimum knee flexion angle at initial contact (p = 0.000), a significantly greater minimum (initial contact) hip flexion angle at initial contact (p = 0.009), a significantly smaller sagittal plane total energy dissipation (p = 0.028), a significantly smaller frontal plane total energy dissipation (p = 0.008), a significantly smaller lower limb total energy dissipation (p = 0.017) than NS during the landing phase. SPM analysis revealed that BS depicted a significantly smaller knee joint vertical reaction force during the 13.8-19.8% landing phase (p = 0.01), a significantly smaller anterior tibia shear force during the 14.2-17.5% landing phase (p = 0.024) than NS. BS appears to change lower limb kinematics at initial contact and then readjust the landing strategies for joint work and joint reaction force, thereby reducing the risk of lower limb skeletal muscle injury. BS have great potential for future development and application uses, which may help athletes to reduce lower limb injury risk.

Biomechanical Characteristics between Bionic Shoes and Normal Shoes during the Drop-Landing Phase: A Pilot Study.

With the development of unstable footwear, more research has focused on the advantages of this type of shoe. This type of shoe could improve the muscle function of the lower limb and prevent injury risks in dynamic situations. Therefore, the purpose of this study was to investigate differences in lower-limb kinetics and kinematics based on single-leg landing (SLL) using normal shoes (NS) and bionic shoes (BS). The study used 15 male subject volunteers (age 23.4 ± 1.14 years, height 177.6 ± 4.83cm, body weight (BW) 73.6 ± 7.02 kg). To ensure the subject standardization of the participants, there were several inclusion criteria used for selection. There were two kinds of experimental shoes used in the landing experiment to detect the change of lower limbs when a landing task was performed. Kinetics and kinematic data were collected during an SLL task, and statistical parametric mapping (SPM) analysis was used to evaluate the differences between NS and BS. We found that the flexion and extension angles of the knee (p = 0.004) and hip (p = 0.046, p = 0.018) joints, and the dorsiflexion and plantarflexion of ankle (p = 0.031) moment were significantly different in the sagittal planes. In the frontal plane, the eversion and inversion of the ankle (p = 0.016), and the abduction and adduction of knee (p = 0.017, p = 0.007) angle were found significant differences. In the horizontal plane, the external and internal rotation of hip (p = 0.036) and knee (p < 0.001, p = 0.029) moment were found significant differences, and knee angle (p = 0.043) also. According to our results, we conclude that using BS can cause bigger knee and hip flexion than NS. Also, this finding indicates that BS might be considered to reduce lower-limb injury risk during the SLL phase.